{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第二周作业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import useful libs\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read the dataset\n",
    "data = pd.read_csv('diabetes.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Split out 20% of the train and test data randomly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "y = data['Outcome']\n",
    "X = data.drop('Outcome',axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=33 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((614, 8), (154, 8))"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Feature Engineering and Data explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Data exploring function\n",
    "def EDA(data, show=True):\n",
    "    \"\"\"\n",
    "    Print out head, info, shape, describe of the data\n",
    "    param:\n",
    "    data: The data needed to be explored\n",
    "    show: True or false to print out the result\n",
    "    \"\"\"\n",
    "    if(show) :\n",
    "        print(data.shape)\n",
    "        print(data.info())\n",
    "        print(data.head())\n",
    "        print(data.describe())\n",
    "    \n",
    "def display_countplot(data, data_name, show=True):\n",
    "    import seaborn as sns\n",
    "    \"\"\"\n",
    "    Draw the countplot of data\n",
    "    param:\n",
    "    data: A list of value\n",
    "    data_name: The name of the data\n",
    "    show: True or false to print out the plot\n",
    "    \"\"\"\n",
    "    if(show):\n",
    "        sns.countplot(data)\n",
    "        pyplot.xlabel(data_name)\n",
    "        pyplot.ylabel('Number of occurences')\n",
    "        pyplot.show()\n",
    "\n",
    "def display_distplot(data, data_name, show=True):\n",
    "    import seaborn as sns\n",
    "    \"\"\"\n",
    "    Draw the distribution plot of data\n",
    "    param:\n",
    "    data: a list of data \n",
    "    data_name: the name of the data\n",
    "    show: True or false to print out the plot\n",
    "\n",
    "    \"\"\"\n",
    "    if(show):\n",
    "        sns.distplot(data)\n",
    "        pyplot.xlabel(data_name)\n",
    "        pyplot.ylabel('Number of occurences')\n",
    "        pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 9)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n",
      "None\n",
      "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
      "0            6      148             72             35        0  33.6   \n",
      "1            1       85             66             29        0  26.6   \n",
      "2            8      183             64              0        0  23.3   \n",
      "3            1       89             66             23       94  28.1   \n",
      "4            0      137             40             35      168  43.1   \n",
      "\n",
      "   DiabetesPedigreeFunction  Age  Outcome  \n",
      "0                     0.627   50        1  \n",
      "1                     0.351   31        0  \n",
      "2                     0.672   32        1  \n",
      "3                     0.167   21        0  \n",
      "4                     2.288   33        1  \n",
      "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
      "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
      "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
      "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
      "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
      "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
      "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
      "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
      "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
      "\n",
      "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
      "count  768.000000                768.000000  768.000000  768.000000  \n",
      "mean    31.992578                  0.471876   33.240885    0.348958  \n",
      "std      7.884160                  0.331329   11.760232    0.476951  \n",
      "min      0.000000                  0.078000   21.000000    0.000000  \n",
      "25%     27.300000                  0.243750   24.000000    0.000000  \n",
      "50%     32.000000                  0.372500   29.000000    0.000000  \n",
      "75%     36.600000                  0.626250   41.000000    1.000000  \n",
      "max     67.100000                  2.420000   81.000000    1.000000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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QT3b0vQxYARwGGBCSNItN98jRj09tJ3kFcC5wNnAF8PGd7SdJmh2mnYNIcijwPuAMYDVw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\n/x/B8DV0RrtiXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1108c3a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fzFOhbM6oIdQcbeXtrTaGPNS0tnt4fUsZw1PiQqp218XThzN/TDq/enM7NUet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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19a66860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set show flag\n",
    "show = True\n",
    "# Explore the data\n",
    "EDA(data)\n",
    "\n",
    "display_countplot(y,'Outcome')\n",
    "display_distplot(y,'Outcome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1109519e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glucose\n"
     ]
    },
    {
     "data": {
      "image/png": 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OllaSGpvsKGMJaYO+nnRk5RV/r5Ifsb1BfinnYfIj2OyyQXZEk42fbWDZ6WJr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19efaf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BloodPressure\n"
     ]
    },
    {
     "data": {
      "image/png": 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rJ1UI0+g4OoA4LXqWqAwudPcr6NjZPWBmU/MztxMzeUugf54GV+WnZXwXoi1z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\nICIiSRWCiIgA8P8B84zbKz/BAasAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19db6fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SkinThickness\n"
     ]
    },
    {
     "data": {
      "image/png": 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oq/xzviEl71WdzjJIq/9R4fEcpGRwWzZ8GOmQrRj+HOmwON/bnko6h92fdHFp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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b0f2da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Insulin\n"
     ]
    },
    {
     "data": {
      "image/png": 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8BpP2MYt8ADdTtHzXAH9RWv/XKIJM3i4Wy9yhlG9tlHljpOdjtNyibouylLf3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a199b06d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BMI\n"
     ]
    },
    {
     "data": {
      "image/png": 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6G/8QH6e088texN5E+VFx3K7fDXRQvlPqIdmBhM+QHBUE4ZpEpSPMSsEqHFXG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\n1/8HNQh15jFN9OgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110c22eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DiabetesPedigreeFunction\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10838ba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age\n"
     ]
    },
    {
     "data": {
      "image/png": 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o/ycwIGIfQVpGT83PA/H5OmBZyXQ+PabpYuDGkm6zWdsPDeVGplPMTINKyieR\nFq7Gup0XM+mgRoZ3TswMg0gZ9GFSdu4UP9LQ6K8HaeXYgbR79no2PFIy+UXUmUFauC3XxlWk3bip\nuTq/IG3tdSKt7B6P8k+TdhmnAhfGTPBFIhFEP58gJYMHgZ1KfuhtGin/HenwT5dGumVbvTNICevv\nwDrRbWm+Dukwl5ESy58i7p2iv29HnWNIK5d8G3fHuOaHdSvwuej+w/gNdiIdI50e0/AnpJXunaSt\nwLnAb6LOh4kAOBT4TYz/Kokguj0d478vDSvirE5H4HbSgvzxGP/+0W1pSZ1jo3x90op7Zm46XRPd\nBrHqeYqsnbtJySAr/ydpDw/S/PFyfN6bdF7hsRj/J0nnMvahIRFkh18WZd2yctJycGC+PLpdRDp0\n0r20TvzPDj/sQ1oGto/yl0nz9z40LANbkjak/pSL7SnSlq2Rzn+9V9L+aODb+fZJy0B2iOVE0kp+\nH9Iy8G/SHsMo0gr3StIhoiWkFV027yyP8j5RPjU+XwmcH/30ISWTSRHflSV1PkdaPv9N2ht8mHRo\naiVpntssV+egKN8rpsF0Gs41jIlux5MSbmk742IaZOVjIq7PkfYUXo7y/Drgx6QNpR75REA6ivA6\nqyaGTUmHpe8i7XnfThzKbVeJIEbuXmILpaTbQ6TDBiNKyk8gLTBnNTG8h4B58X1n0lbYbNIWwvvx\n42zRVJ0ou4d08u9eUgZ/Htg8uq1DWqlNysdG2uJaN1dncSPxbUfaI/lO/OjZCnrvqHcOcGbpD50v\nj8+3Ax1y388saedLpC2Yc0grg9nx9wFpK7yxOvtGnTNJW2C9o9xIC2fWfjfSgvyxXPvfIe3qZsP6\nZEz3rM6FMd7zaTiReGP8n0PDlvz7pK24rP+VMa2XkXa/Ia1U340687NuuTr5Yb0V/SyKYXn8b6zO\nm7n2nyGt7LNuH5TUmRv9Zu3fFW1l/S+MOjeUTOODSMd5z43pPBPoGd16xvdzWXUemERa8XxYTtoj\n+xewYXxfpU6UbRVx/JCGZWB2jP/bxEn+XP+9SSu/LLZ7gH1z3d8EzilZBnrl24/hWm6+eS8X8xbR\n/nakrekvxnR7DnglNw1ejvKs/2z8v0jDxQTfIs2T2fh/MV8nN/4vABNj/F+JcV8ZbQxopE7vqHNX\njP+gXLfnSRsB+dgWkA7NZe0vzvVvpD2Du0qm88nA27nvM2O8B8S4zsx1+1W0kY3n/xIbTi1NBGvs\nOYI4Rnwt6aTe5Y102x54Md/NzA4GLgducveLc+Xb5ob3AWl3Hnd/krTV8zfgt6SZ4TPuPt/Meubq\nQEoGmdujnRmkrdcsWwMcQMPbOTjXAAAFfElEQVSVF/m450a/M0hbSLMith3MrIuZdSCtNJdFPw8C\nx5tZF9KCPSGG/YyZbR7lkK4SyMpPJB2DPsndPzCzDaLbTDPbM9ox0i7qRsBUd9+CdOXSbtH2zrnh\n/VeuzmDSHtQzpKTUL9o/kLQX80x8P570LKnlufZnAF3MbI/opy9pT+sZM+vu7meTjp9PB75HuqLm\naNLCc6a7947f7Gl3P8bdz3b3XqQV6/Do/5gY/45A16hzJOkE6LGk47i9SMd7/0paIDd19y3cvYu7\nr0NaMA/Ihke6simr8wBpfjsmfv8p0e040rmIB7LYSFt1N+TaH0BKDPtFXNcDCyLm7ma2kZl1A86K\nbgeRVrrjgRPNbOPcPHAQ8FTU2Tg3D2TlB5NOqh7l7svMbKNct11ydYbEb/BIjN/OEdurpJXPv2IZ\nyNoZGL9jFtsE0pVgmNmuMT89HMPuB8xy9zn59knH8w/OzQP/ibi6u/t80vJ3CemQy/6kPbvbgJVm\ntn1Mg1diPsj63yCGtz/wdIz/STQcNyc3rI2BV2JY/UnJ4lF37+7unyAlz/mkQ8e7Rx3L1RlIXO0U\n88Bu0e0rpHVAn1xsS4GX3H1Orv1XgcUxrP1IifFpM+se07ED6VzlWBqMj/E+inSO8Y7o9+CYzm/F\nd4t2ZtAaRbJFLf5IJ2GctNudXY7XN36MhdEtu0Qx6zYnyrPLHV+P8nGkTO7R//RcnXw7K0i75n1J\nJyGfj25vk2bkrM6+Ub6cNGM8B/SNuCc0EffJudiyy+D6ks72L6fhZPKPYjjbRCzvkbZYp+e6XUTD\npYP/Ia3QIW3NvELDZaILSIcnOtBweVp2OeoFuWm9S3R/P8Yza+fhkjo/ifLPxzTJtt5/nRvWIzE9\nnigZ1hm53+Ud4Ioov5Q0886Mfval4XDKNqStw+dIW0PZMfpv0bBH8DoNh1lWxm+WTffrSMm3AymR\nPBkx3Q/c08g8V3po6IGSOtllsl1ISepJ0spjGKsenppEWuHlhzWQhsM+02i4fPTSGL/lpBXFdNKF\nCJD2rv5Bw6WDz+S6nUTD+ZaVpBUvMay5uWn9Zq7OPbnyJTScv9km4no8hnlBlP8x2swua5yZG9b2\npJVQNrxrc+M/Nn6fx0vG5wga5s0P5xvSce5nSedL5pHmndtJhz66kebD5RHzXVE+kDR/f0BaBhZE\n+XOkZWBmtPFGbljjovsy0nrgHmDTXNzZ5a3Tc3X+SNpoWxbT4O4oX4+U7J8jzc/PZ3ViWONjfPLj\n8gVSQsjO3z0Y5dn4z4pps0kupm7RX3aoq2vud87ONy6P3/mPpMOY+eVjLnDN6ta3urNYRKTOrbGH\nhkREpDqUCERE6pwSgYhInVMiEBGpc0oEIiJ1TolAZDXMbKCZuZl9utaxiFSCEoHI6h1FehTFkbUO\nRKQSlAhEmmFmnUg30A0jEoGZdTCz35jZdDO708wmmNmQ6LaHmT1kZlPN7F4z61nD8EUKUSIQad5h\npLuQnwXeNLPPkJ4x05v0OI4TSc+CwszWJT3OeIi770G6s/mCWgQt0hI1eXm9yFrkKNKTTCE9PfIo\n0sMDb/H0Pob5ZvZgdN+e9ETV+9OjX+hIPC9fZE2mRCDShHgQ3H7ATmbmxMt8SA9Ca7QKMN3d965S\niCJtQoeGRJo2BPiDu2/l7r3jCZUvkh50NzjOFfQgPVwO0oPONjezDw8Vmdl/1SJwkZZQIhBp2lGU\nb/2PI70QZg7pqaS/JT0d8213X0FKHhebWfaU0f+uXrgiraOnj4q0gpl18vQO6G6kR2V/Pp5DL7LW\n0TkCkda5M14OtB7pFYlKArLW0h6BiEid0zkCEZE6p0QgIlLnlAhEROqcEoGISJ1TIhARqXNKBCIi\nde7/A/BKkZ/b93xlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11065f198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore other features \n",
    "# Show the countplot of all the features\n",
    "for feature_name in X.columns:\n",
    "    print(feature_name)\n",
    "    display_countplot(X[feature_name], feature_name, show)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面的countplot可以看出，有些数据的分布非常不平均，集中在某些值上，下面我们来看一看在distplot的形状"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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c/T1gGdA3xFhDN3X+Orp1yGRQfvtEhyINqHtOFnedP5DXbhzDmcd04aG3VzB6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\nCxERiUnJQkREYlKyEBGRmP4/aXmGnlqRXFoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a53b518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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T7nXzq8SEOjezti4D9gO/Be4DCkTkEhfnLgFy/F5nA4e7a+OMvaQClT0cWwKU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\nnubn5wc7DDMEVJWr/vAWh08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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19e43748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zQRtKa8lO9THXWcfEvJ/P62FqrvW7xAs3zWJbARv6YswQNh6ss/6WIczKT6ey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\njDEm5Cy5GGOMCbn/D7c4KvG5ISczAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1086a5fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kISKcWVrA6+X19IyyTtSGfU28sauB00vzSfCPekL/hHFqSR4d3X1srjwU6VCM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a4cafd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4aETsTYu/AcwG5pOc5XypvaAiYnFELIiIBePHj+/8CHrJM1uSEE+YNLJPPq83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\nJxgzM8vF/w8azDktbc7VvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1985a128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Llq4+lhZOnCaoQSuLs6lv7+FoY5fboRgT1XIfXwh9/VLYNQXmRHheFRC+AM8soGaEMlUi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\nEVmyMMYYE9H/ByjVoCy4oYfBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1098ba390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sVLnPdbi/vtfjFEVbLSLPuvt5XNzZ5CPtfyQickDcUgnuGdkz7vKt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\njImwpGCMMSbi/wOVNz3yGVnRfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a4cdd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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k8gS7jtSzfNNBKo83sv3QcdbtrwEgLTGGSSNTmHJOKqMyEy1xGOMTP5NFBZAf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\nJQtjjDEh/X/dcAV4Yd8q9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a7ff7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the distribution plot of all features\n",
    "for feature_name in X.columns:\n",
    "    display_distplot(X[feature_name], feature_name, show)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看出，有些特征由于是缺失值，被用0来填补了，尝试用中位数填充\n",
    "'DiabetesPedigreeFunction'可以使用log变换来处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Fill the na data with mediam or mean\n",
    "feature_list_to_fill_na = [\n",
    "    'BloodPressure',\n",
    "    'SkinThickness',\n",
    "    'Insulin',\n",
    "    'BMI'\n",
    "]\n",
    "from sklearn.preprocessing import Imputer\n",
    "imputer = Imputer(missing_values=0, copy=False, axis=0, strategy='median')\n",
    "X[feature_list_to_fill_na] = imputer.fit_transform(X[feature_list_to_fill_na])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Log the np.log(X.DiabetesPedigreeFunction)\n",
    "X['DiabetesPedigreeFunction'] = np.log(X.DiabetesPedigreeFunction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use PolynomialFeatures \n",
    "# from sklearn.preprocessing import PolynomialFeatures\n",
    "# poly = PolynomialFeatures()\n",
    "# feature_list = ['Glucose','BloodPressure','SkinThickness','Insulin']\n",
    "# X_train_poly = poly.fit_transform(X_train[feature_list])\n",
    "# X_test_poly = poly.transform(X_test[feature_list])\n",
    "\n",
    "X['Insulin'] = X['Insulin']**3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# Binarize the feature of Pregancies\n",
    "from sklearn.preprocessing import binarize\n",
    "Is_pregnanted = binarize(X.Pregnancies.reshape(-1,1), threshold=1)\n",
    "Is_pregnanted\n",
    "X.Pregnancies = Is_pregnanted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Feature scaling by MinMaxScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mm_scaler = MinMaxScaler()\n",
    "X_train_mm = mm_scaler.fit_transform(X_train)\n",
    "X_test_mm = mm_scaler.transform(X_test)\n",
    "\n",
    "# Feature Standard Scaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_scaler = StandardScaler()\n",
    "X_train_ss = ss_scaler.fit_transform(X_train)\n",
    "X_test_ss = ss_scaler.transform(X_test)\n",
    "# After test, standard scaler is better for model training.\n",
    "# So in following steps, use X_train_ss as training data.\n",
    "X_train = X_train_ss\n",
    "X_test = X_test_ss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Logistic Regression, find best Regulization function and parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [ 0.54055371  0.55624049  0.5112034   0.42931716  0.45784559]\n",
      "cv logloss is: 0.499032070324\n"
     ]
    }
   ],
   "source": [
    "# Use logistic regression and cv\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "loss = cross_val_score(lr, X_train, y_train, cv=5, scoring='neg_log_loss')\n",
    "print('logloss of each fold is: ', -loss)\n",
    "print('cv logloss is:', -loss.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Regulization\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "penalties = ['l1', 'l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty=penalties, C=Cs)\n",
    "\n",
    "lr_penalty = LogisticRegression()\n",
    "grid = GridSearchCV(lr_penalty, tuned_parameters, cv=5, scoring='neg_log_loss')\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 0.00090785,  0.00081139,  0.00065036,  0.00074072,  0.00071936,\n",
       "         0.00090213,  0.00086074,  0.00092297,  0.00109963,  0.00083671,\n",
       "         0.00103998,  0.00083017,  0.00080843,  0.00077763]),\n",
       " 'mean_score_time': array([ 0.00095072,  0.00068221,  0.00079627,  0.00085893,  0.00061555,\n",
       "         0.00066061,  0.0006465 ,  0.00066323,  0.00086184,  0.00068111,\n",
       "         0.00071397,  0.00066247,  0.00063539,  0.00059252]),\n",
       " 'mean_test_score': array([-0.69314718, -0.64832626, -0.68511143, -0.54507724, -0.50658881,\n",
       "        -0.50014635, -0.49932077, -0.49909915, -0.49947454, -0.49945449,\n",
       "        -0.49949923, -0.49949819, -0.49950436, -0.49950265]),\n",
       " 'mean_train_score': array([-0.69314718, -0.64652157, -0.68226073, -0.53742582, -0.49443712,\n",
       "        -0.48610026, -0.48250323, -0.48236018, -0.48229885, -0.48229732,\n",
       "        -0.48229666, -0.48229664, -0.48229663, -0.48229663]),\n",
       " 'param_C': masked_array(data = [0.001 0.001 0.01 0.01 0.1 0.1 1 1 10 10 100 100 1000 1000],\n",
       "              mask = [False False False False False False False False False False False False\n",
       "  False False],\n",
       "        fill_value = ?),\n",
       " 'param_penalty': masked_array(data = ['l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2'],\n",
       "              mask = [False False False False False False False False False False False False\n",
       "  False False],\n",
       "        fill_value = ?),\n",
       " 'params': [{'C': 0.001, 'penalty': 'l1'},\n",
       "  {'C': 0.001, 'penalty': 'l2'},\n",
       "  {'C': 0.01, 'penalty': 'l1'},\n",
       "  {'C': 0.01, 'penalty': 'l2'},\n",
       "  {'C': 0.1, 'penalty': 'l1'},\n",
       "  {'C': 0.1, 'penalty': 'l2'},\n",
       "  {'C': 1, 'penalty': 'l1'},\n",
       "  {'C': 1, 'penalty': 'l2'},\n",
       "  {'C': 10, 'penalty': 'l1'},\n",
       "  {'C': 10, 'penalty': 'l2'},\n",
       "  {'C': 100, 'penalty': 'l1'},\n",
       "  {'C': 100, 'penalty': 'l2'},\n",
       "  {'C': 1000, 'penalty': 'l1'},\n",
       "  {'C': 1000, 'penalty': 'l2'}],\n",
       " 'rank_test_score': array([14, 12, 13, 11, 10,  9,  2,  1,  4,  3,  6,  5,  8,  7], dtype=int32),\n",
       " 'split0_test_score': array([-0.69314718, -0.6489089 , -0.68010129, -0.55482   , -0.53129968,\n",
       "        -0.53189611, -0.54001714, -0.54055371, -0.54227847, -0.54234678,\n",
       "        -0.5425363 , -0.54254197, -0.54255977, -0.54256166]),\n",
       " 'split0_train_score': array([-0.69314718, -0.64563669, -0.67801074, -0.53116438, -0.48416625,\n",
       "        -0.47502622, -0.47087048, -0.47072263, -0.4706497 , -0.47064814,\n",
       "        -0.47064735, -0.47064733, -0.47064732, -0.47064732]),\n",
       " 'split1_test_score': array([-0.69314718, -0.66051983, -0.67819123, -0.59138208, -0.5573063 ,\n",
       "        -0.55838999, -0.55578339, -0.55624049, -0.55625394, -0.55631724,\n",
       "        -0.55632327, -0.5563304 , -0.5563342 , -0.55633178]),\n",
       " 'split1_train_score': array([-0.69314718, -0.64030571, -0.66590191, -0.52335952, -0.48060975,\n",
       "        -0.47233776, -0.46888701, -0.46872872, -0.46867002, -0.46866837,\n",
       "        -0.46866775, -0.46866773, -0.46866772, -0.46866772]),\n",
       " 'split2_test_score': array([-0.69314718, -0.6486946 , -0.68249362, -0.54851852, -0.51533464,\n",
       "        -0.50935525, -0.51127904, -0.5112034 , -0.51201179, -0.51201435,\n",
       "        -0.51210715, -0.5121064 , -0.51211842, -0.51211572]),\n",
       " 'split2_train_score': array([-0.69314718, -0.64598197, -0.68225864, -0.53509135, -0.49002919,\n",
       "        -0.48218953, -0.47833112, -0.47818319, -0.47811583, -0.47811433,\n",
       "        -0.4781136 , -0.47811358, -0.47811357, -0.47811357]),\n",
       " 'split3_test_score': array([-0.69314718, -0.64090057, -0.69168919, -0.51150211, -0.46245697,\n",
       "        -0.44002167, -0.4317005 , -0.42931716, -0.42853335, -0.42825108,\n",
       "        -0.42816737, -0.42814508, -0.42814556, -0.42813448]),\n",
       " 'split3_train_score': array([-0.69314718, -0.65101939, -0.69198517, -0.55103718, -0.51169765,\n",
       "        -0.50482025, -0.50189402, -0.50177842, -0.50173206, -0.5017306 ,\n",
       "        -0.50173011, -0.5017301 , -0.50173009, -0.50173009]),\n",
       " 'split4_test_score': array([-0.69314718, -0.64256053, -0.69314718, -0.51895109, -0.46621823,\n",
       "        -0.46074841, -0.45748366, -0.45784559, -0.45795763, -0.45800603,\n",
       "        -0.4580249 , -0.45802997, -0.45802662, -0.45803244]),\n",
       " 'split4_train_score': array([-0.69314718, -0.64966408, -0.69314718, -0.54647665, -0.50568275,\n",
       "        -0.49612755, -0.49253354, -0.49238796, -0.49232663, -0.49232513,\n",
       "        -0.49232448, -0.49232446, -0.49232445, -0.49232445]),\n",
       " 'std_fit_time': array([  2.37712119e-04,   1.69872620e-04,   1.53639966e-04,\n",
       "          4.18497348e-05,   3.53417728e-05,   1.12503096e-04,\n",
       "          1.06651898e-04,   2.88717650e-04,   1.37529823e-04,\n",
       "          4.40682675e-05,   2.22130382e-04,   7.37513105e-05,\n",
       "          2.56640297e-05,   3.32672441e-05]),\n",
       " 'std_score_time': array([  3.58233562e-04,   5.87068849e-05,   2.60553414e-04,\n",
       "          2.57076467e-04,   5.62175438e-05,   9.65897666e-05,\n",
       "          8.78217805e-05,   9.88727798e-05,   1.87961768e-04,\n",
       "          7.87672925e-05,   1.15570769e-04,   9.72239979e-05,\n",
       "          3.85993715e-05,   2.30581214e-05]),\n",
       " 'std_test_score': array([ 0.        ,  0.00689433,  0.00612313,  0.02850201,  0.03697201,\n",
       "         0.04394412,  0.04756939,  0.04838741,  0.04893879,  0.04903949,\n",
       "         0.04910008,  0.04910833,  0.04911337,  0.04911525]),\n",
       " 'std_train_score': array([ 0.        ,  0.00373676,  0.00998752,  0.01009656,  0.01216969,\n",
       "         0.01247653,  0.0127651 ,  0.01277749,  0.01278398,  0.01278404,\n",
       "         0.01278411,  0.01278412,  0.01278412,  0.01278412])}"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the complete results\n",
    "grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.499099149287\n",
      "{'C': 1, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# examine the best model\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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BCd0ncP+o+wnxa+QhUZKXOd/7TmTZyv2EB/lxeq92jRuDUqpB6tymISJPiUgb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\n/kfaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19c0c9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV residuals curve\n",
    "test_means = grid.cv_results_['mean_test_score']\n",
    "test_stds = grid.cv_results_['std_test_score']\n",
    "train_means = grid.cv_results_['mean_train_score']\n",
    "train_stds = grid.cv_results_['std_train_score']\n",
    "\n",
    "# # plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penalties = len(penalties)\n",
    "test_scores = np.array(test_means).reshape(n_Cs, number_penalties)\n",
    "train_scores = np.array(train_means).reshape(n_Cs, number_penalties)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs, number_penalties)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs, number_penalties)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penalties) :\n",
    "    pyplot.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i], \n",
    "                   label=penalties[i]+'Test')\n",
    "    pyplot.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i],\n",
    "                   label=penalties[i]+'Train')    \n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.xlabel('log(C)')\n",
    "pyplot.ylabel('neg_logloss')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图给出了L1正则和L2正则下、不同正则参数C对应的模型在训练集上测试集上的正确率（score）。可以看出在训练集上C越大（正则越少）的模型性能越好；但在测试集上当C=0.1时性能最好（L1正则和L2正则均是)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use LogisticRegressionCV to implement regulariezed Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### L1 RegularizationCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: array([[-0.68010129, -0.5312989 , -0.54001593, -0.54228157],\n",
       "        [-0.67819123, -0.55731379, -0.55578605, -0.55625088],\n",
       "        [-0.68249362, -0.51533321, -0.51127741, -0.51201055],\n",
       "        [-0.69168919, -0.46246283, -0.43169222, -0.42854053],\n",
       "        [-0.69314718, -0.46621972, -0.45748284, -0.45795856]])}"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "Cs=[0.01, 0.1, 1, 10]\n",
    "lrcv_L1 = LogisticRegressionCV(Cs=Cs, cv=5, scoring='neg_log_loss',\n",
    "                               solver='liblinear',\n",
    "                               penalty='l1')\n",
    "lrcv_L1.fit(X_train, y_train)\n",
    "# y_test_predict = lrcv_L1.predict(X_test)\n",
    "lrcv_L1.scores_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### L2 Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: array([[-0.55482   , -0.53189611, -0.54055371, -0.54234678],\n",
       "        [-0.59138208, -0.55838999, -0.55624049, -0.55631724],\n",
       "        [-0.54851852, -0.50935525, -0.5112034 , -0.51201435],\n",
       "        [-0.51150211, -0.44002167, -0.42931716, -0.42825108],\n",
       "        [-0.51895109, -0.46074841, -0.45784559, -0.45800603]])}"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrcv_L2 = LogisticRegressionCV(Cs=Cs, cv=5, scoring='neg_log_loss',\n",
    "                               solver='liblinear',\n",
    "                               penalty='l2')\n",
    "lrcv_L2.fit(X_train, y_train)\n",
    "# y_test_predict = lrcv_L1.predict(X_test)\n",
    "lrcv_L2.scores_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 4. Linear SVM and regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Default SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import metrics\n",
    "SVC1 = LinearSVC().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for classifier LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.76      0.94      0.84       100\n",
      "          1       0.81      0.46      0.59        54\n",
      "\n",
      "avg / total       0.78      0.77      0.75       154\n",
      "\n",
      "\n",
      "Confution matrix:\n",
      "[[94  6]\n",
      " [29 25]]\n"
     ]
    }
   ],
   "source": [
    "# test the model on test set, evaluate the model\n",
    "y_predict = SVC1.predict(X_test)\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "     % (SVC1, metrics.classification_report(y_test, y_predict)))\n",
    "print(\"Confution matrix:\\n%s\" % (metrics.confusion_matrix(y_test, y_predict)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVC with Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fit_grid_point_Linear(C, X_train, y_train, X_test, y_test):\n",
    "    \n",
    "    # train SVC with train data set\n",
    "    SVC = LinearSVC(C=C)\n",
    "    SVC = SVC.fit(X_train, y_train)\n",
    "    \n",
    "    accuracy = SVC.score(X_test, y_test)\n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.7662337662337663\n",
      "accuracy: 0.7727272727272727\n",
      "accuracy: 0.7857142857142857\n",
      "accuracy: 0.512987012987013\n",
      "accuracy: 0.7142857142857143\n",
      "accuracy: 0.6753246753246753\n",
      "accuracy: 0.7207792207792207\n"
     ]
    },
    {
     "data": {
      "image/png": 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BDwDnmlkfM+sDnBvvEym5KAoNyS+/nHYkwbZtMHMmTJgQemuJlEJiycLddwDX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\nSF5KFiIikpeShYiI5KVkISIief1/c9b5v4k1dg0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110927080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# parameter list of C\n",
    "C_s = np.logspace(-3, 10, 7)\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    tmp = fit_grid_point_Linear(oneC, X_train, y_train, X_test, y_test)\n",
    "    accuracy_s.append(tmp)\n",
    "\n",
    "x_axis = np.log10(C_s)\n",
    "pyplot.plot(x_axis, np.array(accuracy_s), 'b-')\n",
    "pyplot.legend()\n",
    "pyplot.xlabel('log(C)')\n",
    "pyplot.ylabel('accuracy')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.SVM of RBF Kernal, and regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "def fit_grid_point_RBF(C, gama, X_train, y_train, X_test, y_test):\n",
    "    \n",
    "    # train the svc model by train data set\n",
    "    svc = SVC(C=C, kernel='rbf', gamma=gamma)\n",
    "    svc = svc.fit(X_train, y_train)\n",
    "    \n",
    "    # get the accuracy on test set\n",
    "    accuracy = svc.score(X_test, y_test)\n",
    "    print(\"C: {}, gamma: {} accuracy: {}\".format(C, gamma, accuracy))\n",
    "    return accuracy\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C: 0.01, gamma: 0.01 accuracy: 0.6493506493506493\n",
      "C: 0.01, gamma: 0.1 accuracy: 0.6493506493506493\n",
      "C: 0.01, gamma: 1.0 accuracy: 0.6493506493506493\n",
      "C: 0.01, gamma: 10.0 accuracy: 0.6493506493506493\n",
      "C: 0.01, gamma: 100.0 accuracy: 0.6493506493506493\n",
      "C: 0.1, gamma: 0.01 accuracy: 0.6493506493506493\n",
      "C: 0.1, gamma: 0.1 accuracy: 0.7467532467532467\n",
      "C: 0.1, gamma: 1.0 accuracy: 0.6493506493506493\n",
      "C: 0.1, gamma: 10.0 accuracy: 0.6493506493506493\n",
      "C: 0.1, gamma: 100.0 accuracy: 0.6493506493506493\n",
      "C: 1.0, gamma: 0.01 accuracy: 0.7662337662337663\n",
      "C: 1.0, gamma: 0.1 accuracy: 0.7597402597402597\n",
      "C: 1.0, gamma: 1.0 accuracy: 0.7012987012987013\n",
      "C: 1.0, gamma: 10.0 accuracy: 0.6493506493506493\n",
      "C: 1.0, gamma: 100.0 accuracy: 0.6493506493506493\n",
      "C: 10.0, gamma: 0.01 accuracy: 0.7662337662337663\n",
      "C: 10.0, gamma: 0.1 accuracy: 0.7857142857142857\n",
      "C: 10.0, gamma: 1.0 accuracy: 0.6883116883116883\n",
      "C: 10.0, gamma: 10.0 accuracy: 0.6493506493506493\n",
      "C: 10.0, gamma: 100.0 accuracy: 0.6493506493506493\n",
      "C: 100.0, gamma: 0.01 accuracy: 0.7662337662337663\n",
      "C: 100.0, gamma: 0.1 accuracy: 0.7532467532467533\n",
      "C: 100.0, gamma: 1.0 accuracy: 0.6883116883116883\n",
      "C: 100.0, gamma: 10.0 accuracy: 0.6493506493506493\n",
      "C: 100.0, gamma: 100.0 accuracy: 0.6493506493506493\n"
     ]
    }
   ],
   "source": [
    "# Paramater list of Cs and Gammas\n",
    "C_s = np.logspace(-2, 2, 5)\n",
    "gamma_s = np.logspace(-2, 2, 5)\n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    for j, gamma in enumerate(gamma_s):\n",
    "        tmp = fit_grid_point_RBF(oneC, gamma, X_train, y_train, X_test, y_test)\n",
    "        accuracy_s.append(tmp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述部分运行结果来看，gamma参数设置不太合适，（gamma 越大，对应的RBF核的sigma越小，决策边界更复杂，可能发生了过拟合）， 所以调小gamma值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Paramater list of Cs and Gammas\n",
    "# C_s = np.logspace(-2, 2, 4)\n",
    "# gamma_s = np.logspace(-2, 2, 4)\n",
    "\n",
    "# accuracy_s = []\n",
    "# for i, oneC in enumerate(C_s):\n",
    "#     for j, gamma in enumerate(gamma_s):\n",
    "#         tmp = fit_grid_point_RBF(oneC, gamma, X_train, y_train, X_test, y_test)\n",
    "#         accuracy_s.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
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bWvY3uxrTLNgUz6UrGQzW82trWq4KsmdRFdiklPrOOj+FvvC8ONm2AE7tgk5v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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11065f9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the score on the combination of C and Gamma list\n",
    "accuracy_s1 = np.array(accuracy_s).reshape(len(C_s), len(gamma_s))\n",
    "x_axis = np.log10(C_s)\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    pyplot.plot(x_axis, np.array(accuracy_s1[:,j]), \n",
    "                 label='Test - log(gamma)' + str(np.log10(gamma)))\n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.xlabel('log(C)')\n",
    "pyplot.ylabel('Accuracy')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，C=10， gamma=0.1 时，得到的精确度最高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
